Spatial Network Databases Research Articles

Efficient processing of distance–time k th‐order skyline queries in bicriteria networks

With the increasing complexity of traffic conditions in road networks, the nearest destination cannot be necessarily reached in the fastest time. The traditional nearest neighbor (NN) and kNN searches in spatial network databases with single cost criterion are often strongly restrictive. In this paper, the authors consider the problem of kth-order skyline queries in bicriteria networks, where edges represent road segments. Their proposed kth-order skyline queries consider distance, time preferences of each edge, thus having two kinds of skyline queries, named distance optimal kth-order skyline query (DO-kOSQ) and time optimal kth-OSQ (TO-kOSQ). They design algorithms for the two kinds of skyline queries in bicriteria networks based on incremental network expansion method and further develop a maximum distance/time restriction strategy to improve the efficiency of the algorithms. Experimental results show that all of their methods are far below 1000 input–output input/output (IO) accesses and 1 s of central processing unit (CPU) time. For real road networks, their kOSQ+ queries need only 51.6% IO accesses and 59.6% CPU time of those for kOSQ queries, whereas for the larger road network the percentages are 51.8% and 51.2%, respectively. Thus, the results indicate the efficiency and effectiveness of their proposed methods.

Efficient Processing of All-farthest-neighbors Queries in Spatial Network Databases

Efficient Processing of All-farthest-neighbors Queries in Spatial Network Databases

Efficient Processing of Spatial Preference Queries in Spatial Network Databases

Efficient Processing of Spatial Preference Queries in Spatial Network Databases

Validation of TRMM and FEWS Satellite Rainfall Estimates with Rain Gauge Measurement over Ashanti Region, Ghana

Satellite rainfall estimates have predominantly been used for climate impact studies due to poor rain gauge network in sub-Saharan Africa. However, there are limited microscale studies within the sub-region that have assessed the performance of these satellite products, which is the focus of the present study. This paper therefore considers validation of Tropical Rainfall Measuring Mission (TRMM) and Famine Early Warning System (FEWS) satellite estimates with rain gauge measurements over Ashanti region of Ghana. First, a consistency assessment of the two gauge data products, the Automatic Rain Gauge (ARG) and Ghana Meteorological Agency (GMet) Standard Rain Gauge (SRG) measurements, was performed. This showed a very good agreement with correlation coefficient of 0.99. Secondly, satellite rainfall products from TRMM and FEWS were validated with the two gauge measurements. Validation results showed good agreement with correlation coefficients of 0.6 and 0.7 for TRMM and FEWS with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. Probability Of Detection (POD) and Volumetric Hit Index (VHI) were found to be greater than 0.9. Volumetric Critical Success Index (VCSI) was 0.9 and 0.8 for TRMM and FEWS respectively with low False Alarm Ratio (FAR) and insignificant Volumetric Miss Index (VMI). In general, relatively low biases and RMSE values were observed. The biases were less than 1.3 and 0.8 for TRMM and FEWS-RFE respectively. These indicate high rainfall detection capabilities of both satellite products. In addition, both TRMM and FEWS were able to capture the onset, peak and cessation of the rainy season, as well as the dry spells. Although TRMM and FEWS sometimes under/overestimated rainfall, they have the potential to be used for agricultural and other hydro-climatic impact studies over the region. The Dynamic-Aerosol-Cloud-Chemistry Interactions in West Africa (DACCIWA) project will provide an improved spatial gauge network database over the study area to enhance future validation and other climate impact studies.

On k-path covers and their applications

For a directed graph G with vertex set V we call a subset C ⊆ V a k-(All-)Path Cover if C contains a node from any path consisting of k nodes. This paper considers the problem of constructing small k -Path Covers in the context of road networks with millions of nodes and edges. In many application scenarios the set C and its induced overlay graph constitute a very compact synopsis of G which is the basis for the currently fastest data structure for personalized shortest path queries, visually pleasing overlays of subsampled paths, and efficient reporting, retrieval and aggregation of associated data in spatial network databases. Apart from a theoretical investigation of the problem, we provide efficient algorithms that produce very small k -Path Covers for large real-world road networks (with a posteriori guarantees via instance-based lower bounds).

Finding Alternative Shortest Paths in Spatial Networks

Shortest path query is one of the most fundamental queries in spatial network databases. There exist algorithms that can process shortest path queries in real time. However, many complex applications require more than just the calculation of a single shortest path. For example, one of the common ways to determine the importance (or price) of a vertex or an edge in spatial network is to use Vickrey pricing, which intuitively values the vertex v (or edge e ) based on how much harder for travelling from the sources to the destinations without using v (or e ). In such cases, the alternative shortest paths without using v (or e ) are required. In this article, we propose using a precomputation based approach for both single pair alternative shortest path and all pairs shortest paths processing. To compute the alternative shortest path between a source and a destination efficiently, a naïive way is to precompute and store all alternative shortest paths between every pair of vertices avoiding every possible vertex (or edge), which requires O ( n 4 ) space. Currently, the state of the art approach for reducing the storage cost is to choose a subset of the vertices as center points, and only store the single-source alternative shortest paths from those center points. Such approach has the space complexity of O ( n 2 log n ). We propose a storage scheme termed iSPQF , which utilizes shortest path quadtrees by observing the relationships between each avoiding vertex and its corresponding alternative shortest paths. We have reduced the space complexity from the naïive O ( n 4 ) (or the state of the art O ( n 4 log n )) to O (min( γ, L ) n 1.5 ) with comparable query performance of O ( K ), where K is the number of vertices in the returned paths, L is the diameter of the spatial network, and γ is a value that depends on the structure of the spatial network, which is empirically estimated to be 40 for real road networks. Experiments on real road networks have shown that the space cost of the proposed iSPQF is scalable, and both the algorithms based on iSPQF are efficient.

Approximate range query processing in spatial network databases

Spatial range query is one of the most common queries in spatial databases, where a user invokes a query to find all the surrounding interest objects. Most studies in range search consider Euclidea...

Integrated GNSS with different accuracy of track database for safety-critical railway control systems

The primary object of this research is to test and to assess a tightly coupled GNSS and railway track data system for railway applications. One of the key features of this system is its ability to take into account uncertainties in both GPS observables and the spatial railway network data. Since trains travel on pre-defined fixed routes, 3-D positioning becomes a 1-D positioning problem that fixes the position of the train along the track. The spatial railway network database is then used to estimate the other two coordinates. In this way, the accuracy of the railway network database is crucial for the integrated positioning system. Unfortunately, the necessary accuracy of the railway network database is not known. Additionally, the integrity and availability performance of GNSS-based train location system are also essential for the safety-critical railway control systems. The required navigation performances (RNP) for the safety-critical railway applications in terms of accuracy, integrity and availability are investigated. We test different accuracies of the track database for the integrated system. Based on the result, when the GPS is integrated with the track database, the accuracy, integrity and availability are improved. The higher the accuracy of the track database, the better the positioning accuracy is guaranteed. For the integrity and availability performance, the result shows that the medium accuracy of the track database is more cost-effective for the integrated system.

부분순위 연속 k 최근접 객체 탐색 기법

In the application areas of LBS(Location Based Service) and ITS(Intelligent Transportation System), continuous k-nearest neighbor query(CkNN) which is defined as a query to find the nearest points of interest to all the points on a given path is widely used. It is necessary to acquire results quickly in the above applications and be applicable to spatial network databases. It is also able to cope successfully with frequent updates of POI objects. This paper proposes a new method to search nearest POIs for moving query objects on the spatial networks. The method produces a set of split points and their corresponding k-POIs as results with partial order among k-POIs. The results obtained from experiments with real dataset show that the proposed method outperforms the existing methods. The proposed method achieves very short processing time(15%) compared with the existing method.

TSC-IRNN: Time- and Space-Constraint In-Route Nearest Neighbor Query Processing Algorithms in Spatial Network Databases

Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.

Spatial Network RNN Queries in GIS

Geographical information systems (GIS) and applications assist us in commuting, traveling and locating our points of interests. The efficient implementation and support of spatial queries in those systems is of particular interest and importance. The use of a Voronoi diagram has traditionally been applied to computational geometry. In this paper, we will show how a Voronoi diagram can be applied to support spatial queries in GIS systems, and in particular to reverse nearest neighbor (RNN) queries. An RNN query retrieves the set of interest objects having the query object as the nearest neighbor among other objects. Two cases of RNN queries are: monochromatic (MRNN) and bichromatic (BRNN). In the MRNN, the interest objects and the query object are of the same type, whereas in the BRNN they are of two different types. Due to the shortcomings of solutions for BRNN in the literature, we develop a new approach and algorithm, named the ‘2Vor BRNN algorithm’, for processing this query type in the context of the spatial network database (SNDB). Our novel approach extends the previous work and uses the ‘order-2 network Voronoi diagram’ to provide a more efficient solution for the BRNN. In addition, we experimentally confirm that the proposed algorithm outperforms the previous one in terms of memory used and response time.

Bichromatic Reverse Nearest-Neighbor Search in Mobile Systems

In traditional reverse nearest-neighbor (RNN) search, the goal is to find the set of interest objects taking the query object as the nearest neighbor given that the interest objects and the query object have the same type. However, when the interest objects and the query object are of different types, this problem is called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bichromatic reverse nearest neighbor</i> (BRNN) search. The BRNN query has an increasing number of mobile applications and requires efficient algorithms for processing. In this paper, we present a novel approach for the BRNN search in the context of spatial network databases (SNDB). The main idea behind our approaches is to use Euclidean-based range search to prune the search space and use Euclidean distance in addition to network distance to minimize processing time. Finally, the experimental results confirm that our proposed algorithm have good performance on different network densities.

Processing spatial skyline queries in both vector spaces and spatial network databases

In this article, we first introduce the concept of Spatial Skyline Queries (SSQ). Given a set of data points P and a set of query points Q , each data point has a number of derived spatial attributes each of which is the point's distance to a query point. An SSQ retrieves those points of P which are not dominated by any other point in P considering their derived spatial attributes. The main difference with the regular skyline query is that this spatial domination depends on the location of the query points Q . SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in P and Q . Consequently, we reduce the complexity of SSQ search from O (| P | 2 | Q |) to O (| S | 2 | C | + √| P |), where | S | and | C | are the solution size and the number of vertices of the convex hull of Q , respectively. Considering Euclidean distance, we propose two algorithms, B 2 S 2 and VS 2 , for static query points and one algorithm, VCS 2 , for streaming Q whose points change location over time (e.g., are mobile). VCS 2 exploits the pattern of change in Q to avoid unnecessary recomputation of the skyline and hence efficiently perform updates. We also propose two algorithms, SNS 2 and VSNS 2 , that compute the spatial skyline with respect to the network distance in a spatial network database. Our extensive experiments using real-world datasets verify that both R-tree-based B 2 S 2 and Voronoi-based VS 2 outperform the best competitor approach in terms of both processing time and I/O cost. Furthermore, their output computed based on Euclidean distance is a good approximation of the spatial skyline in network space. For accurate computation of spatial skylines in network space, our experiments showed the superiority of VSNS 2 over SNS 2 .

Spatial Relationship Networks: Network Theory Applied to GIS Data

This paper examines a novel approach to geospatial feature database production and maintenance using network theory to derive new feature content to augment existing Geographic Information System (GIS) databases. After recording an extensive set of commonly occurring feature associations, we hypothesize that it is possible to predict new feature content using existing geographic information system GIS feature data. In preparation to test this hypothesis, an extensive set of quantitative spatial relationship classes were defined to describe a wide variety of common-place qualitative spatial relations. At the same time, a network of spatial associations was created—a collection of logical axioms describing commonly occurring feature associations, their spatial relationships, and their probability of existence. This knowledge base was stored as a formal ontology using Web Ontology Language-Resource Description Framework (OWL-RDF). To test the hypothesis, selected real-world GIS features were used to extract first- and second-order associations from the spatial association network database. Using the spatial relationships linked with each derived feature association, discrete locations were computed for potentially new features—new feature content that could be used to augment the existing GIS data. This preliminary prototyping indicates it is possible to accurately predict discrete locations of new feature content. However, further development is necessary to expand the spatial association network dataset and to perform follow-on testing of the feature prediction capability.

Group K-Nearest Neighbors queries in spatial network databases

This paper addresses the problem of Group K-Nearest Neighbors (GKNN) queries in spatial network databases, and suggests a novel approach based on real network distances. This approach essentially uses network Voronoi diagram properties together with a progressive incremental network expansion for determining the inner network distances that are needed to obtain GKNN queries.

A shortest path algorithm for moving objects in spatial network databases

One of the most important kinds of queries in Spatial Network Databases (SNDB) to support location-based services (LBS) is the shortest path query. Given an object in a network, e.g. a location of a car on a road network, and a set of objects of interests, e.g. hotels, gas station, and car, the shortest path query returns the shortest path from the query object to interested objects. The studies of shortest path query have two kinds of ways, online processing and preprocessing. The studies of preprocessing suppose that the interest objects are static. This paper proposes a shortest path algorithm with a set of index structures to support the situation of moving objects. This algorithm can transform a dynamic problem to a static problem. In this paper we focus on road networks. However, our algorithms do not use any domain specific information, and therefore can be applied to any network. This algorithm’s complexity is O( k log 2 i ), and traditional Dijkstra’s complexity is O(( i + k) 2).

GML 문서 저장을 위한 저장 스키마 및 하부 저장 관리자의 설계 및 구현

GML은 OGC(OpenGIS Consortium)에서 공간지리정보의 저장 및 전송을 위한 인코딩 표준으로 제안한 마크업 언어이다. 일반적인 공간 네트워크 데이터베이스에서 GML 지원을 위한 연구는 GML 문서의 파싱, GML 문서의 저장, GML 문서의 질의어로 분류된다. 이러한 3가지 주제 가운데 GML 문서 저장에 관한 연구는 효율적인 GML 문서 검색을 위해 필수적인 연구이다. 그러나 기존 XML 문서의 저장 스키마를 다루는 연구는 다수인 데 반해, GML 문서의 저장 스키마에 관한 연구는 거의 전무한 형편이다. 또한 기존 XML 문서 저장 스키마는 공간지리정보 저장에 적합하지 않다. 따라서 본 논문에서는 기존의 XML저장방식의 단점인 많은 중복데이터 저장, 엘리먼트를 얻기 위해 여러 테이블을 탐색해야 하는 단점을 보완하는 GML 문서의 효율적 저장을 위한 3가지의 저장 스키마를 제안한다. 아울러 제안하는 저장 스키마에 적합한 GML 지원 하부 저장 관리자를 선계 및 구현한다. GML is a markup language presented as exchange standard for geographic information by the OGC(Open GIS Consortium). In spatial network databases, researches for supporting GML(Geographic Markup Language) can be divided into the parsing, the storing and the retrieval of GML documents. Among them, the study on the storage of GML documents is essential for their efficient retrieval. However there is little research on the storing of GML documents whereas there have been a lot of researches on the storing of n documents. Because the storage schema designed for XML documents are not appropriate for geographic information, we, in this paper, propose three storage schema for efficiently storing GML documents including geographic information in order to solve the problem that the XML storage schema store duplicate data and need to search many tables for obtaining elements. In addition, we design and implement a low-level storage manager which can store GML documents using the proposed GML storage schema.

EDAR Algorithm for Continuous KNN Queries Based on Pine

The continuous K nearest neighbor (CKNN) query is an important type of query that continuously finds the KNN to a query point on a given path. In this article we focus on moving queries issued on stationary objects in spatial network database (SNDB). The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object travelling on one interval of the path remains the same all through that interval until it reaches a split point where its KNNs change. Existing methods for CKNN are based on Euclidean distances. In this article, we propose a new algorithm for answering CKNN in SNDB where the important measure for the shortest path is network distances rather than Euclidean distances. We propose DAR and eDAR algorithms to address CKNN queries based on the progressive incremental network expansion (PINE) technique. Our experiments show that the eDAR approach has better response time, and requires fewer shortest distance computations and KNN queries than approaches that are based on VN3 using IE.

공간 네트워크 데이터베이스를 위한 저장 및 색인 구조의 설계 및 성능평가

For supporting LBS service, recent studies on spatial network databases (SNDB) have been done actively. In order to gain good performance on query processing in SNDB, we, in this paper. design efficient storage and index structures for spatial network data, point of interests (POIs), and moving objects on spatial networks. First, we design a spatial network file organization for maintaining the spatial network data itself consisting of both node and edges. Secondly, we design a POI storage and index structure which is used for gaining fast accesses to POIs, like restaurant, hotel, and gas station. Thirdly, we design a signature-based storage and index structure for efficiently maintaining past, current, and expected future trajectory information of moving objects. Finally, we show that the storage and index structures designed in this paper outperform the existing storage structures for spatial networks as well as the conventional trajectory index structures for moving objects.

Alternative Solutions for Continuous K Nearest Neighbor Queries in Spatial Network Databases

Continuous K nearest neighbor queries (C-KNN) are defined as finding the nearest points of interest along an enitre path (e.g., finding the three nearest gas stations to a moving car on any point of a pre-specified path). The result of this type of query is a set of intervals (or split points) and their corresponding KNNs, such that the KNNs of all points within each interval are the same. The current studies on C-KNN focus on vector spaces where the distance between two objects is a function of their spatial attributes (e.g., Euclidean distance metric). These studies are not applicable to spatial network databases (SNDB) where the distance between two objects is a function of the network connectivity (e.g., shortest path between two objects). In this paper, we propose two techniques to address C-KNN queries in SNDB: Intersection Examination (IE) and Upper Bound Algorithm (UBA). With IE, we first find the KNNs of all nodes on a path and then, for those adjacent nodes whose nearest neighbors are different, we find the intermediate split points. Finally, we compute the KNNs of the split points using the KNNs of the surrounding nodes. The intuition behind UBA is that the performance of IE can be improved by determining the adjacent nodes that cannot have any split points in between, and consequently eliminating the computation of KNN queries for those nodes. Our empirical experiments show that the UBA approach outperforms IE, specially when the points of interest are sparsely distributed in the network.